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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Purposedifferent
Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication.When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify.
Complexitydifferent
HighMedium
Timedifferent
30-90 min Setup, danach laufend30-60 min
Participantsdifferent
1-82-6
Formatsame
Workshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationSmaller Stories, Acceptance Criteria, Split Rationale
Tags1 shared
ForecastingFlowDelivery
BacklogIterationDelivery
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation